Reverse engineering ontologies from performance systems

D Richards*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Considerable effort is associated with the development, validation and integration of ontologies. This paper suggests that an alternative, or possibly complementary approach, to engineering ontologies is to retrospectively and automatically discover them from existing data and knowledge sources in the organization and then to combine them if desired. The method offered assists in the identification of similar and different terms and includes strategies for developing a shared ontology. The approach uses a data analysis technique known as formal concept analysis to generate an ontology. The approach is particularly strong when used in conjunction with a rapid and incremental knowledge acquisition and representation technique, known as ripple-down rules. However, any data that can be converted into a crosstable (a binary decision table) can also use the approach. The ontological representation is not as rich as many others but we have found it useful for uncovering higher-level concepts and structure that were not explicit in the performance data. If richer models are required our approach may provide a quick way of developing a first draft and gaining initial ontological commitment.

Original languageEnglish
Title of host publicationResearch and development in intelligent system XIX
EditorsM Bramer, A Preece, F Coenen
Place of PublicationLondon
PublisherSpringer, Springer Nature
Pages193-206
Number of pages14
ISBN (Print)1852336749
Publication statusPublished - 2003
Event22nd SGAI International Conference on Knowledge Based Systems and Applied Artificial Intelligence - Peterhouse College, Cambridge, United Kingdom
Duration: 10 Dec 200212 Dec 2002

Publication series

NameB C S CONFERENCE SERIES
PublisherSPRINGER-VERLAG LONDON LTD

Conference

Conference22nd SGAI International Conference on Knowledge Based Systems and Applied Artificial Intelligence
CountryUnited Kingdom
CityCambridge
Period10/12/0212/12/02

Cite this